A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 18th international conference on World wide web
CrowdReranking: exploring multiple search engines for visual search reranking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Query aware visual similarity propagation for image search reranking
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Boosted Multiple Kernel Learning for Scene Category Recognition
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Discovering joint audio---visual codewords for video event detection
Machine Vision and Applications
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Re-ranking the returned images from a query relies on two important steps to improve its effectiveness: the estimation of the image relevance and the enhancement of the similarity function. However, attaining an effective visual similarity and an accurate re-ranking are quite challenging. We address these issues by first evaluating the image relevance to the query from the dataset according to the visual features and the co-occurrence of local patches of images. Then we boost the visual similarity measure associated with image relevance, and propose an enhancement algorithm, called Boosting-MKL, which not only incrementally learns the feature fusion but also generally preserves the initial local ranking. Specifically, we perform a random walk over a similarity graph for re-ranking. The experimental results demonstrate that our proposed approach significantly improves the effectiveness of visual similarity measure and the performance of image reranking.